Added support of a "--nvlink-path" option in clang-nvlink-wrapper which
takes the path of nvlink binary.
Static Device Library support for OpenMP (D105191) now searches for
nvlink binary and passes its location via this option. In absence
of this option, nvlink binary is searched in locations in PATH.
Differential Revision: https://reviews.llvm.org/D111488
An archive containing device code object files can be passed to
clang command line for linking. For each given offload target
it creates a device specific archives which is either passed to llvm-link
if the target is amdgpu, or to clang-nvlink-wrapper if the target is
nvptx. -L/-l flags are used to specify these fat archives on the command
line. E.g.
clang++ -fopenmp -fopenmp-targets=nvptx64 main.cpp -L. -lmylib
It currently doesn't support linking an archive directly, like:
clang++ -fopenmp -fopenmp-targets=nvptx64 main.cpp libmylib.a
Linking with x86 offload also does not work.
Reviewed By: ye-luo
Differential Revision: https://reviews.llvm.org/D105191
An archive containing device code object files can be passed to
clang command line for linking. For each given offload target
it creates a device specific archives which is either passed to llvm-link
if the target is amdgpu, or to clang-nvlink-wrapper if the target is
nvptx. -L/-l flags are used to specify these fat archives on the command
line. E.g.
clang++ -fopenmp -fopenmp-targets=nvptx64 main.cpp -L. -lmylib
It currently doesn't support linking an archive directly, like:
clang++ -fopenmp -fopenmp-targets=nvptx64 main.cpp libmylib.a
Linking with x86 offload also does not work.
Reviewed By: ye-luo
Differential Revision: https://reviews.llvm.org/D105191
This allows clang to work on Linux distributions like Debian where
<CUDA-PATH>/include may be a symlink to /usr/include. We only need
`cuda_wrappers` to be present before the standard C++ library headers.
The CUDA SDK headers themselves do not need to be found that early.
This addresses https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=995122
mentioned in post-commit comments on D108247
Differential Revision: https://reviews.llvm.org/D110596
Always use cuda.h to detect CUDA version. It's a more universal approach
compared to version.txt which is no longer present in recent CUDA versions.
Split the 'unknown CUDA version' warning in two:
* when detected CUDA version is partially supported by clang. It's expected to
work in general, at the feature parity with the latest supported CUDA
version. and may be missing support for the new features/instructions/GPU
variants. Clang will issue a warning.
* when detected version is new. Recent CUDA versions have been working with
clang reasonably well, and will likely to work similarly to the partially
supported ones above. Or it may not work at all. Clang will issue a warning and
proceed as if the latest known CUDA version was detected.
Differential Revision: https://reviews.llvm.org/D108247
Attempt to enable MemCpyOpt unconditionally in D104801 uncovered the fact that
there are users that do not expect LLVM to materialize `memset` intrinsic.
While other passes can do that, too, MemCpyOpt triggers it more frequently and
breaks sanitizers and some downstream users.
For now introduce a flag to force-enable the flag and opt-in only CUDA
compilation with NVPTX back-end.
Differential Revision: https://reviews.llvm.org/D106401
Clang diagnostics should not start with a capital letter or use
trailing punctuation (https://clang.llvm.org/docs/InternalsManual.html#the-format-string),
but quite a few driver diagnostics were not following this advice. This
corrects the grammar and punctuation to improve consistency, but does
not change the circumstances under which the diagnostics are produced.
Moving `InputInfo.h` from `lib/Driver/` into `include/Driver` to be able to expose it in an API consumed from outside of `clangDriver`.
Reviewed By: dexonsmith
Differential Revision: https://reviews.llvm.org/D106787
This patch adds a driver flag `-fopenmp-target-new-runtime` to optionally enable the new device runtime
bitcode library. This allows users to enable the new experimental runtime
before it becomes the default in the future.
Reviewed By: jdoerfert
Differential Revision: https://reviews.llvm.org/D106793
https://lists.llvm.org/pipermail/openmp-dev/2021-March/003940.html reports
test failure in `openmp-offload-gpu.c`. The failure is, when using `-S` in the
clang driver, it still reports bitcode library doesn't exist. However, it is not
exposed in my local run and Phabiractor test. The reason it escaped from Phabricator
test is, the test machine doesn't have CUDA, so `LibDeviceFile` is empty. In this
case, the check of `OPT_S` will be hit, and we get "expected" result. However, if
the test machine has CUDA, `LibDeviceFile` will not be empty, then the check will
not be done, and it just proceeds, trying to add the bitcode library. The reason
it escaped from my local run is, I didn't build ALL targets, so this case was
marked UNSUPPORTED.
Reviewed By: kkwli0
Differential Revision: https://reviews.llvm.org/D98902
In D97003, CUDA 9.2 is the minimum requirement for OpenMP offloading on
NVPTX target. We don't need to have macros in source code to select right functions
based on CUDA version. we don't need to compile multiple bitcode libraries of
different CUDA versions for each SM. We don't need to worry about future
compatibility with newer CUDA version.
`-target-feature +ptx61` is used in this patch, which corresponds to the highest
PTX version that CUDA 9.2 can support.
Reviewed By: jdoerfert
Differential Revision: https://reviews.llvm.org/D97198
In current implementation of `deviceRTLs`, we're using some functions
that are CUDA version dependent (if CUDA_VERSION < 9, it is one; otheriwse, it
is another one). As a result, we have to compile one bitcode library for each
CUDA version supported. A worse problem is forward compatibility. If a new CUDA
version is released, we have to update CMake file as well.
CUDA 9.2 has been released for three years. Instead of using various weird tricks
to make `deviceRTLs` work with different CUDA versions and still have forward
compatibility, we can simply drop support for CUDA 9.1 or lower version. It has at
least two benifits:
- We don't need to generate bitcode libraries for each CUDA version;
- Clang driver doesn't need to search for the bitcode lib based on CUDA version.
We can claim that starting from LLVM 12, OpenMP offloading on NVPTX target requires
CUDA 9.2+.
Reviewed By: jdoerfert, JonChesterfield
Differential Revision: https://reviews.llvm.org/D97003
This patch uses the existing logic of CUDA for searching libomptarget
and extracts it to a common method.
Reviewed By: JonChesterfield, tianshilei1992
Differential Revision: https://reviews.llvm.org/D96248
The patch only plumbs through the option necessary for targeting sm_86 GPUs w/o
adding any new functionality.
Differential Revision: https://reviews.llvm.org/D95974
Currently -fgpu-rdc is not passed to host clang -cc1.
This causes issue because -fgpu-rdc affects shadow
variable linkage in host compilation.
Reviewed by: Artem Belevich
Differential Revision: https://reviews.llvm.org/D96105
From this patch (plus some landed patches), `deviceRTLs` is taken as a regular OpenMP program with just `declare target` regions. In this way, ideally, `deviceRTLs` can be written in OpenMP directly. No CUDA, no HIP anymore. (Well, AMD is still working on getting it work. For now AMDGCN still uses original way to compile) However, some target specific functions are still required, but they're no longer written in target specific language. For example, CUDA parts have all refined by replacing CUDA intrinsic and builtins with LLVM/Clang/NVVM intrinsics.
Here're a list of changes in this patch.
1. For NVPTX, `DEVICE` is defined empty in order to make the common parts still work with AMDGCN. Later once AMDGCN is also available, we will completely remove `DEVICE` or probably some other macros.
2. Shared variable is implemented with OpenMP allocator, which is defined in `allocator.h`. Again, this feature is not available on AMDGCN, so two macros are redefined properly.
3. CUDA header `cuda.h` is dropped in the source code. In order to deal with code difference in various CUDA versions, we build one bitcode library for each supported CUDA version. For each CUDA version, the highest PTX version it supports will be used, just as what we currently use for CUDA compilation.
4. Correspondingly, compiler driver is also updated to support CUDA version encoded in the name of bitcode library. Now the bitcode library for NVPTX is named as `libomptarget-nvptx-cuda_[cuda_version]-sm_[sm_number].bc`, such as `libomptarget-nvptx-cuda_80-sm_20.bc`.
With this change, there are also multiple features to be expected in the near future:
1. CUDA will be completely dropped when compiling OpenMP. By the time, we also build bitcode libraries for all supported SM, multiplied by all supported CUDA version.
2. Atomic operations used in `deviceRTLs` can be replaced by `omp atomic` if OpenMP 5.1 feature is fully supported. For now, the IR generated is totally wrong.
3. Target specific parts will be wrapped into `declare variant` with `isa` selector if it can work properly. No target specific macro is needed anymore.
4. (Maybe more...)
Reviewed By: JonChesterfield
Differential Revision: https://reviews.llvm.org/D94745
D94700 removed the static library so we no longer need to pass
`-llibomptarget-nvptx` to `nvlink`. Since the bitcode library is the only device
runtime for now, instead of emitting a warning when it is not found, an error
should be raised. We also set a new option `libomptarget-nvptx-bc-path` to let
user choose which bitcode library is being used.
Reviewed By: JonChesterfield
Differential Revision: https://reviews.llvm.org/D95161
Summary:
Optimized debugging is not supported by ptxas. Debugging information is degraded to line information only if optimizations are enabled, but debugging information would be added back in by the driver if remarks were enabled. This solves https://bugs.llvm.org/show_bug.cgi?id=48153.
Reviewers: jdoerfert tra jholewinski serge-sans-paille
Differential Revision: https://reviews.llvm.org/D94123
If CUDA version can not be determined based on version.txt file, attempt to find
CUDA_VERSION macro in cuda.h.
This is a follow-up to D89752,
Differntial Revision: https://reviews.llvm.org/D89832
CUDA-11.1 does not carry version.txt which causes clang to assume that it's
CUDA-7.0, which used to be the only CUDA version w/o version.txt.
In order to tell CUDA-7.0 apart from the new versions, clang now probes for the
presence of libdevice.10.bc which is not present in the old CUDA versions.
This should keep Clang working for CUDA-11.1.
PR47332: https://bugs.llvm.org/show_bug.cgi?id=47332
Differential Revision: https://reviews.llvm.org/D89752
Object of class `Command` contains various properties of a command to
execute, but output file was missed from them. This change adds this
property. It is required for reporting consumed time and memory implemented
in D78903 and may be used in other cases too.
Differential Revision: https://reviews.llvm.org/D78902
In CUDA/HIP a function may become implicit host device function by
pragma or constexpr. A host device function is checked in both
host and device compilation. However it may be emitted only
on host or device side, therefore the diagnostics should be
deferred until it is known to be emitted.
Currently clang is only able to defer certain diagnostics. This causes
false alarms and limits the usefulness of host device functions.
This patch lets clang defer all overloading resolution diagnostics for host device functions.
An option -fgpu-defer-diag is added to control this behavior. By default
it is off.
It is NFC for other languages.
Differential Revision: https://reviews.llvm.org/D84364
specified at Command creation, rather than as part of the Tool.
This resolves the hack I just added to allow Darwin toolchain to vary
its level of support based on `-mlinker-version=`.
The change preserves the _current_ settings for response-file support.
Some tools look likely to be declaring that they don't support
response files in error, however I kept them as-is in order for this
change to be a simple refactoring.
Differential Revision: https://reviews.llvm.org/D82782
It's useful for using clang from tools that may need need to provide SDK files
from non-standard locations.
Clang CLI only provides a way to specify VFS for include files, so there's no
good way to test this yet.
Differential Revision: https://reviews.llvm.org/D81771
To support std::complex and some other standard C/C++ functions in HIP device code,
they need to be forced to be __host__ __device__ functions by pragmas. This is done
by some clang standard C++ wrapper headers which are shared between cuda-clang and hip-Clang.
For these standard C++ wapper headers to work properly, specific include path order
has to be enforced:
clang C++ wrapper include path
standard C++ include path
clang include path
Also, these C++ wrapper headers require device version of some standard C/C++ functions
must be declared before including them. This needs to be done by including a default
header which declares or defines these device functions. The default header is always
included before any other headers are included by users.
This patch adds the the default header and include path for HIP.
Differential Revision: https://reviews.llvm.org/D81176
I didn't realize HIP was a distinct offloading kind, so the subtarget
was looking for -march, which isn't correct for HIP. We also have the
possibility of different denormal defaults in the case of multiple
offload targets, so we need to thread the JobAction through the target
hook.
Generate PTX using newer versions of PTX and allow using sm_80 with CUDA-11.
None of the new features of CUDA-10.2+ have been implemented yet, so using these
versions will still produce a warning.
Differential Revision: https://reviews.llvm.org/D77670
Instead of hardcoding individual GPU mappings in multiple functions, keep them
all in one table and use it to look up the mappings.
We also don't care about 'virtual' architecture much, so the API is trimmed down
down to a simpler GPU->Virtual arch name lookup.
Differential Revision: https://reviews.llvm.org/D77665
The argument after -Xarch_device will be added to the arguments for CUDA/HIP
device compilation and will be removed for host compilation.
The argument after -Xarch_host will be added to the arguments for CUDA/HIP
host compilation and will be removed for device compilation.
Differential Revision: https://reviews.llvm.org/D76520
Extract common code to a function. To prepare for
adding an option for CUDA/HIP host and device only
option.
Differential Revision: https://reviews.llvm.org/D76455
This fixes an issue with clang issuing a warning about unknown CUDA SDK if it's
detected during non-CUDA compilation.
Differential Revision: https://reviews.llvm.org/D76030
AMDGPU and x86 at least both have separate controls for whether
denormal results are flushed on output, and for whether denormals are
implicitly treated as 0 as an input. The current DAGCombiner use only
really cares about the input treatment of denormals.
This is how it should've been and brings it more in line with
std::string_view. There should be no functional change here.
This is mostly mechanical from a custom clang-tidy check, with a lot of
manual fixups. It uncovers a lot of minor inefficiencies.
This doesn't actually modify StringRef yet, I'll do that in a follow-up.
This makes clang somewhat forward-compatible with new CUDA releases
without having to patch it for every minor release without adding
any new function.
If an unknown version is found, clang issues a warning (can be disabled
with -Wno-cuda-unknown-version) and assumes that it has detected
the latest known version. CUDA releases are usually supersets
of older ones feature-wise, so it should be sufficient to keep
released clang versions working with minor CUDA updates without
having to upgrade clang, too.
Differential Revision: https://reviews.llvm.org/D73231
Currently there are 4 different mechanisms for controlling denormal
flushing behavior, and about as many equivalent frontend controls.
- AMDGPU uses the fp32-denormals and fp64-f16-denormals subtarget features
- NVPTX uses the nvptx-f32ftz attribute
- ARM directly uses the denormal-fp-math attribute
- Other targets indirectly use denormal-fp-math in one DAGCombine
- cl-denorms-are-zero has a corresponding denorms-are-zero attribute
AMDGPU wants a distinct control for f32 flushing from f16/f64, and as
far as I can tell the same is true for NVPTX (based on the attribute
name).
Work on consolidating these into the denormal-fp-math attribute, and a
new type specific denormal-fp-math-f32 variant. Only ARM seems to
support the two different flush modes, so this is overkill for the
other use cases. Ideally we would error on the unsupported
positive-zero mode on other targets from somewhere.
Move the logic for selecting the flush mode into the compiler driver,
instead of handling it in cc1. denormal-fp-math/denormal-fp-math-f32
are now both cc1 flags, but denormal-fp-math-f32 is not yet exposed as
a user flag.
-cl-denorms-are-zero, -fcuda-flush-denormals-to-zero and
-fno-cuda-flush-denormals-to-zero will be mapped to
-fp-denormal-math-f32=ieee or preserve-sign rather than the old
attributes.
Stop emitting the denorms-are-zero attribute for the OpenCL flag. It
has no in-tree users. The meaning would also be target dependent, such
as the AMDGPU choice to treat this as only meaning allow flushing of
f32 and not f16 or f64. The naming is also potentially confusing,
since DAZ in other contexts refers to instructions implicitly treating
input denormals as zero, not necessarily flushing output denormals to
zero.
This also does not attempt to change the behavior for the current
attribute. The LangRef now states that the default is ieee behavior,
but this is inaccurate for the current implementation. The clang
handling is slightly hacky to avoid touching the existing
denormal-fp-math uses. Fixing this will be left for a future patch.
AMDGPU is still using the subtarget feature to control the denormal
mode, but the new attribute are now emitted. A future change will
switch this and remove the subtarget features.
Skip distro detection when we're not running on Linux, or when the target triple is not Linux. This saves a few OS calls for each invocation of clang.exe.
Differential Revision: https://reviews.llvm.org/D70467
Carlos Galvez